Human activity recognition with inertial sensors using a deep learning approach

Zebin, Tahmina ORCID: https://orcid.org/0000-0003-0437-0570, Scully, Patricia J. and Ozanyan, Krikor B. (2017) Human activity recognition with inertial sensors using a deep learning approach. In: IEEE Sensors, SENSORS 2016 - Proceedings. The Institute of Electrical and Electronics Engineers (IEEE), USA. ISBN 9781479982875

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Abstract

Our focus in this research is on the use of deep learning approaches for human activity recognition (HAR) scenario, in which inputs are multichannel time series signals acquired from a set of body-worn inertial sensors and outputs are predefined human activities. Here, we present a feature learning method that deploys convolutional neural networks (CNN) to automate feature learning from the raw inputs in a systematic way. The influence of various important hyper-parameters such as number of convolutional layers and kernel size on the performance of CNN was monitored. Experimental results indicate that CNNs achieved significant speed-up in computing and deciding the final class and marginal improvement in overall classification accuracy compared to the baseline models such as Support Vector Machines and Multi-layer perceptron networks.

Item Type:	Book Section
Uncontrolled Keywords:	convolution,convolutional neural networks (cnn),feature extraction,human activity recognition (har),signal processing,electrical and electronic engineering ,/dk/atira/pure/subjectarea/asjc/2200/2208
Related URLs:	http://www.scopus.com/inward/record.url?...
Depositing User:	LivePure Connector
Date Deposited:	28 May 2019 13:30
Last Modified:	06 May 2024 00:03
URI:	https://ueaeprints.uea.ac.uk/id/eprint/71147
DOI:	10.1109/ICSENS.2016.7808590

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